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dc.date.accessioned2020-05-05T18:56:07Z
dc.date.available2020-05-05T18:56:07Z
dc.date.created2019-08-28T11:11:04Z
dc.date.issued2019
dc.identifier.citationCasagrande, Flavia Dias Tørresen, Jim Zouganeli, Evi . Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults. IEEE Access. 2019, 7(1), 111012--111029
dc.identifier.urihttp://hdl.handle.net/10852/75133
dc.description.abstractWe present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 binary sensors each - including motion, magnetic, and power sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence. We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset. The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model. Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup. Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event. Finally, we investigate the performance of predicting daily living activities derived from the sensor events. We can predict activities with an accuracy of 61-90%, depending on the apartment.
dc.languageEN
dc.relation.ispartofCasagrande, Flávia Dias (2019) Sensor Event and Activity Prediction using Binary Sensors in Real Homes with Older Adults. Doctoral thesis http://hdl.handle.net/10852/76622
dc.relation.urihttp://hdl.handle.net/10852/76622
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titlePredicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults
dc.typeJournal article
dc.creator.authorCasagrande, Flavia Dias
dc.creator.authorTørresen, Jim
dc.creator.authorZouganeli, Evi
cristin.unitcode185,15,5,42
cristin.unitnameForskningsgruppe for robotikk og intelligente systemer
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.cristin1719478
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=IEEE Access&rft.volume=7&rft.spage=111012-&rft.date=2019
dc.identifier.jtitleIEEE Access
dc.identifier.volume7
dc.identifier.issue1
dc.identifier.startpage111012
dc.identifier.endpage111029
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2019.2933994
dc.identifier.urnURN:NBN:no-78248
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn2169-3536
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/75133/1/2019%2BIEEE%2BAccess.pdf
dc.type.versionPublishedVersion
dc.relation.projectNFR/247620


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